Apparatus and methods for predicting locations inducing tire pressure changes

ABSTRACT

An apparatus, method and computer program product are provided for predicting road locations that are likely to induce tire pressure changes. In one example, the apparatus receives historical data of an event in which a first vehicle is impacted by a first tire pressure change at a first road segment. The historical data indicates point-of-interest (POI) information associated with the first road segment. The apparatus trains a prediction model using the historical data. The prediction model is configured to determine a likelihood in which a second road segment induces a second tire pressure change for a second vehicle based on POI information associated with the second road segment.

TECHNICAL FIELD

The present disclosure generally relates to the field of vehicle-related data aggregation and analysis, associated methods and apparatus, and in particular, concerns, for example, an apparatus configured to predict a likelihood of a tire pressure change occurring within a location based on contextual information associated with the location.

BACKGROUND

Modern vehicles are equipped with a system, such as a tire pressure monitoring system (TPMS), for detecting a tire pressure drop and informing a user indicating the tire pressure change via a user interface. Generally, the system is limited to the application of detecting and notifying a tire pressure drop and does not provide any assistance in minimizing future occurrences of tire pressure drops.

The listing or discussion of a prior-published document or any background in this specification should not necessarily be taken as an acknowledgement that the document or background is part of the state of the art or is common general knowledge.

BRIEF SUMMARY

According to a first aspect, an apparatus comprising at least one processor and at least one non-transitory memory including computer program code instructions is described. The computer program code instructions, when executed, cause the apparatus to receive historical data of an event in which a first vehicle is impacted by a first tire pressure change at a first road segment. The historical data indicating point-of-interest (POI) information associated with the first road segment. The computer program code instructions, when executed, further causes the apparatus to train a prediction model using the historical data, wherein the prediction model is configured to determine a likelihood in which a second road segment induces a second tire pressure change for a second vehicle based on POI information associated with the second road segment.

According to a second aspect, a non-transitory computer-readable storage medium having computer program code instructions stored therein is described. The computer program code instructions, when executed by at least one processor, cause the at least one processor to cause a prediction model to determine a likelihood in which a first road segment induces a first tire pressure change for a first vehicle based on point-of-interest (POI) information associated with the first road segment, wherein the prediction model is trained using historical data, the historical data indicating an event in which a second vehicle is impacted by a second tire pressure change at a second road segment and POI information associated with the second road segment. The computer program code instructions, when executed by at least one processor, further causes the at least one processor to cause a notification indicating the likelihood on a user equipment associated with the first vehicle.

According to a third aspect, a method of generating a route based on a road location predicted to induce a tire pressure change. The method comprising receiving a destination for a vehicle as input, selecting a subset from a plurality of road segments as the route to the destination, wherein the subset is selected based on a likelihood in which one or more of a plurality of road segments induces a tire pressure change for the vehicle, wherein the likelihood is determined by a prediction model as a function of POI information associated with the one or more of the plurality of road segments; and outputting the route or a portion thereof.

Also, a computer program product may be provided. For example, a computer program product comprising instructions which, when the program is executed by a computer, cause the computer to carry out the steps described herein.

Still other aspects, features, and advantages of the invention are readily apparent from the following detailed description, simply by illustrating a number of particular embodiments and implementations, including the best mode contemplated for carrying out the invention. The invention is also capable of other and different embodiments, and its several details can be modified in various obvious respects, all without departing from the spirit and scope of the invention. Accordingly, the drawings and description are to be regarded as illustrative in nature, and not as restrictive.

The steps of any method disclosed herein do not have to be performed in the exact order disclosed, unless explicitly stated or understood by the skilled person.

Corresponding computer programs (which may or may not be recorded on a carrier) for implementing one or more of the methods disclosed herein are also within the present disclosure and encompassed by one or more of the described example embodiments.

The present disclosure includes one or more corresponding aspects, example embodiments or features in isolation or in various combinations whether or not specifically stated (including claimed) in that combination or in isolation. Corresponding means for performing one or more of the discussed functions are also within the present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments of the invention are illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings:

FIG. 1 illustrates a diagram of a system capable of predicting adverse road locations;

FIG. 2 illustrates a diagram of the database within the system of FIG. 1 ;

FIG. 3 illustrates a diagram of the components of the prediction platform within the system of FIG. 1 ;

FIG. 4 illustrates an example adverse road location and historical data of tire pressure changes associated thereto;

FIG. 5 illustrates an example visual representation indicating an adverse road location;

FIG. 6 illustrates a flowchart of a process for training a machine learning model to predict a likelihood in which a road location induces a tire pressure change;

FIG. 7 illustrates a flowchart of a process for causing a notification indicating a likelihood of a tire pressure change occurring within a road location;

FIG. 8 illustrates a computer system upon which an embodiment may be implemented;

FIG. 9 illustrates a chip set or chip upon which an embodiment may be implemented; and

FIG. 10 illustrates a diagram of exemplary components of a mobile terminal for communications, which is capable of operating in the system of FIG. 1 .

DETAILED DESCRIPTION

Modern vehicles are equipped with a tire pressure monitoring system (TPMS) for monitoring tire pressure levels of a vehicle and alerting a user in response to one or more of the tire pressure levels falling below a threshold. One type of TPMS includes a sensor mounted in a wheel to measure air pressure in each tire. When the air pressure drops below, for example, 25 percent, the sensor transmits a flag to the vehicle's on-board computing platform, and in response, the on-board computing platform triggers a dashboard indicator light. Another type of TPMS integrates Antilock Braking System's (ABS) wheel speed sensors. If a tire pressure is at a “low” level, the wheel speed sensors determine a roll of a tire at a different wheel speed than other tires. Such information is detected by the on-board computing platform, and in response, the on-board computing platform triggers the dashboard indicator light. Generally, applications of the TPMS are limited to detecting and notifying the tire pressure loss and does not provide any assistance in prolonging the longevity of a tire. Since road networks are constantly subject to adverse conditions that render damages to tires, wheels, and components associated thereto, there is a need of a system that provides foresight of locations impacted by such conditions. There will now be described an apparatus and associated methods that may address these issues.

FIG. 1 is a diagram of a system 100 capable of predicting adverse road location, according to one embodiment. Herein, an adverse road location refers to a road location predicted to yield a tire pressure change for a vehicle. The system includes a user equipment (UE) 101, a vehicle 105, a detection entity 113, a services platform 115, content providers 119 a-119 n, a communication network 121, a prediction platform 123, a database 125, and a satellite 127. Additional or a plurality of mentioned components may be provided.

In the illustrated embodiment, the system 100 comprises the user equipment (UE) 101 that may include or be associated with an application 103. In one embodiment, the UE 101 has connectivity to the prediction platform 123 via the communication network 121. The prediction platform 123 performs one or more functions associated with providing a prediction of adverse road locations. In the illustrated embodiment, the UE 101 may be any type of mobile terminal or fixed terminal such as a mobile handset, station, unit, device, multimedia computer, multimedia tablet, Internet node, communicator, desktop computer, laptop computer, notebook computer, netbook computer, tablet computer, personal communication system (PCS) device, personal digital assistants (PDAs), audio/video player, digital camera/camcorder, positioning device, fitness device, television receiver, radio broadcast receiver, electronic book device, game device, devices associated with or integrated with one or more vehicles (including the vehicle 105), or any combination thereof, including the accessories and peripherals of these devices. In one embodiment, the UE 101 can be an in-vehicle navigation system, a personal navigation device (PND), a portable navigation device, a cellular telephone, a mobile phone, a personal digital assistant (PDA), a watch, a camera, a computer, and/or other device that can perform navigation-related functions, such as digital routing and map display. In one embodiment, the UE 101 can be a cellular telephone. A user may use the UE 101 for navigation functions, for example, road link map updates. It should be appreciated that the UE 101 can support any type of interface to the user (such as “wearable” devices, etc.). In one embodiment, the one or more vehicles may have cellular or Wi-Fi connection either through the inbuilt communication equipment or from the UE 101 associated with the vehicles. The application 103 may assist in conveying and/or receiving information regarding at least one attribute associated a road segment via the communication network 121.

In the illustrated embodiment, the application 103 may be any type of application that is executable by the UE 101, such as a mapping application, a location-based service application, a navigation application, a content provisioning service, a camera/imaging application, a media player application, a social networking application, a calendar application, or any combination thereof. In one embodiment, one of the applications 103 at the UE 101 may act as a client for the prediction platform 123 and perform one or more functions associated with the functions of the prediction platform 123 by interacting with the prediction platform 123 over the communication network 121. The application 103 may provide information indicating adverse road locations and/or a route that avoids the adverse road locations.

The vehicle 105 may be a standard gasoline powered vehicle, a hybrid vehicle, an electric vehicle, a fuel cell vehicle, and/or any other mobility implement type of vehicle. The vehicle 105 includes parts related to mobility, such as a powertrain with an engine, a transmission, a suspension, a driveshaft, and/or wheels, etc. The vehicle 105 may be a non-autonomous vehicle or an autonomous vehicle. The term autonomous vehicle may refer to a self-driving or driverless mode in which no passengers are required to be on board to operate the vehicle. An autonomous vehicle may be referred to as a robot vehicle or an automated vehicle. The autonomous vehicle may include passengers, but no driver is necessary. These autonomous vehicles may park themselves or move cargo between locations without a human operator. Autonomous vehicles may include multiple modes and transition between the modes. The autonomous vehicle may steer, brake, or accelerate the vehicle based on the position of the vehicle in order, and may respond to lane marking indicators (lane marking type, lane marking intensity, lane marking color, lane marking offset, lane marking width, or other characteristics) and driving commands or navigation commands. In one embodiment, the vehicle 105 may be assigned with an autonomous level. An autonomous level of a vehicle can be a Level 0 autonomous level that corresponds to a negligible automation for the vehicle, a Level 1 autonomous level that corresponds to a certain degree of driver assistance for the vehicle 105, a Level 2 autonomous level that corresponds to partial automation for the vehicle, a Level 3 autonomous level that corresponds to conditional automation for the vehicle, a Level 4 autonomous level that corresponds to high automation for the vehicle, a Level 5 autonomous level that corresponds to full automation for the vehicle, and/or another sub-level associated with a degree of autonomous driving for the vehicle.

In one embodiment, the UE 101 may be integrated in the vehicle 105, which may include assisted driving vehicles such as autonomous vehicles, highly assisted driving (HAD), and advanced driving assistance systems (ADAS). Any of these assisted driving systems may be incorporated into the UE 101. Alternatively, an assisted driving device may be included in the vehicle 105. The assisted driving device may include memory, a processor, and systems to communicate with the UE 101. In one embodiment, the vehicle 105 may be an HAD vehicle or an ADAS vehicle. An HAD vehicle may refer to a vehicle that does not completely replace the human operator. Instead, in a highly assisted driving mode, a vehicle may perform some driving functions and the human operator may perform some driving functions. Such vehicle may also be driven in a manual mode in which the human operator exercises a degree of control over the movement of the vehicle. The vehicle 105 may also include a completely driverless mode. The HAD vehicle may control the vehicle through steering or braking in response to the on the position of the vehicle and may respond to lane marking indicators (lane marking type, lane marking intensity, lane marking color, lane marking offset, lane marking width, or other characteristics) and driving commands or navigation commands. Similarly, ADAS vehicles include one or more partially automated systems in which the vehicle alerts the driver. The features are designed to avoid collisions automatically. Features may include adaptive cruise control, automate braking, or steering adjustments to keep the driver in the correct lane. ADAS vehicles may issue warnings for the driver based on the position of the vehicle or based on the lane marking indicators (lane marking type, lane marking intensity, lane marking color, lane marking offset, lane marking width, or other characteristics) and driving commands or navigation commands.

In this illustrated example, the vehicle 105 includes a plurality of sensors 107, an on-board computing platform 109, and an on-board communication platform 111. The sensors 107 may include one or more sensors for monitoring tire pressure levels of one or more tires of the vehicle 105. Such sensors and a combination of one or more processors, one or more memory devices, and/or other electronic devices may define a TPMS. By way of example, a direct TPMS may include a battery, a housing, a PCB, a pressure sensor, an analog-digital converter, a microcontroller, a system controller, an oscillator, a radio frequency transmitter, a low frequency receiver, and a voltage regulator. A TPMS may be an electronic system configured to monitor air pressure inside pneumatic tires of a vehicle. The TPMS may report real-time tire-pressure information to a user of the vehicle, either via a gauge, a pictogram display, a simple low-pressure warning light, and/or other types of user interface. TPMS can be divided into two different types— direct (dTPMS) and indirect (iTPMS). The dTPMS directly measures tire pressure using hardware sensors. For example, in the dTPMS, a battery-driven pressure sensor may be mounted inside a valve of each wheel, and said sensor transfers pressure information to the on-board computing platform 109. In one embodiment, one or more sensors of the dTPMS may also measure and alert temperature levels of the tire. In one embodiment, one or more sensors of the dTPMS may utilize a wireless power system. The iTPMS do not use physical pressure sensors but measure air pressures using software-based systems, which by evaluating existing sensor signals like wheel speeds, accelerometers, driveline data, etc. One type of iTPMS systems is based on the principle that under-inflated tires have a slightly smaller diameter (and hence higher angular velocity) than a correctly inflated one. These differences are measurable through the wheel speed sensors of ABS/ESC systems. Another type of iTPMS can also detect simultaneous under-inflation using spectrum analysis of individual wheels, which can be realized in software using advanced signal processing techniques. The sensors 107 may also include other types of sensors, such as image sensors (e.g., electronic imaging devices of both analog and digital types, which include digital cameras, camera modules, camera phones, thermal imaging devices, radar, sonar, lidar, etc.), a global positioning sensor for gathering location data, a network detection sensor for detecting wireless signals or receivers for different short-range communications (e.g., Bluetooth, Wi-Fi, Li-Fi, near field communication (NFC), etc.), temporal information sensors, an audio recorder for gathering audio data, velocity sensors, light sensors, oriental sensors augmented with height sensor and acceleration sensor, tilt sensors to detect the degree of incline or decline of the vehicle 105 along a path of travel, etc. In a further embodiment, sensors about the perimeter of the vehicle 105 may detect the relative distance of the vehicle 105 from road objects (e.g., road markings), lanes, or roadways, the presence of other vehicles, pedestrians, traffic lights, road objects, road features (e.g., curves) and any other objects, or a combination thereof. In one embodiment, the vehicle 105 may include GPS receivers to obtain geographic coordinates from satellites 127 for determining current location and time associated with the vehicle 105. Further, the location can be determined by a triangulation system such as A-GPS, Cell of Origin, or other location extrapolation technologies.

The on-board computing platform 109 performs one or more functions associated with the vehicle 105. In one embodiment, the on-board computing platform 109 may aggregate sensor data generated by at least one of the sensors 107 and transmit the sensor data via the on-board communications platform 111. The on-board computing platform 109 may receive control signals for performing one or more of the functions from the prediction platform 123, the UE 101, the services platform 115, one or more of the content providers 119 a-119 n, or a combination thereof via the on-board communication platform 111. The on-board computing platform 109 includes at least one processor or controller and memory (not illustrated). The processor or controller may be any suitable processing device or set of processing devices such as, but not limited to: a microprocessor, a microcontroller-based platform, a suitable integrated circuit, one or more field programmable gate arrays (FPGAs), and/or one or more application-specific integrated circuits (ASICs). The memory may be volatile memory (e.g., RAM, which can include non-volatile RAM, magnetic RAM, ferroelectric RAM, and any other suitable forms); non-volatile memory (e.g., disk memory, FLASH memory, EPROMs, EEPROMs, non-volatile solid-state memory, etc.), unalterable memory (e.g., EPROMs), read-only memory, and/or high-capacity storage devices (e.g., hard drives, solid state drives, etc). In some examples, the memory includes multiple kinds of memory, particularly volatile memory and non-volatile memory.

The on-board communications platform 109 includes wired or wireless network interfaces to enable communication with external networks. The on-board communications platform 109 also includes hardware (e.g., processors, memory, storage, antenna, etc.) and software to control the wired or wireless network interfaces. In the illustrated example, the on-board communications platform 109 includes one or more communication controllers (not illustrated) for standards-based networks (e.g., Global System for Mobile Communications (GSM), Universal Mobile Telecommunications System (UMTS), Long Term Evolution (LTE) networks, 5G networks, Code Division Multiple Access (CDMA), WiMAX (IEEE 802.16m); Near Field Communication (NFC); local area wireless network (including IEEE 802.11 a/b/g/n/ac or others), dedicated short range communication (DSRC), and Wireless Gigabit (IEEE 802.11ad), etc.). In some examples, the on-board communications platform 109 includes a wired or wireless interface (e.g., an auxiliary port, a Universal Serial Bus (USB) port, a Bluetooth® wireless node, etc.) to communicatively couple with the UE 101.

The detection entity 113 may be another vehicle, a drone, a road-side sensor (e.g., a sensor installed within a road pavement), or a device mounted on a stationary object within or proximate to a road segment (e.g., a traffic light post, a sign post, a post, a building, etc.). The detection entity 113 may be equipped with image sensors (e.g., electronic imaging devices of both analog and digital types, which include digital cameras, camera modules, camera phones, thermal imaging devices, radar, sonar, lidar, etc.), light sensors (e.g., photodetectors), temperature sensors, audio recorder for gathering audio data, velocity sensors, oriental sensors augmented with height sensor and acceleration sensor, tilt sensors, etc. In a further embodiment, sensors about the perimeter of the detection entity 113 may detect the relative distance thereof from road objects (e.g., road markings), lanes, or roadways, the presence of other vehicles, pedestrians, traffic lights, road objects, road features (e.g., curves) and any other objects, or a combination thereof. In one embodiment, the detection entity 113 may include GPS receivers to obtain geographic coordinates from satellites 127 for determining current location and time associated with at which the detection entity 113 acquires sensor data. The location can be determined by a triangulation system such as A-GPS, Cell of Origin, or other location extrapolation technologies. In one embodiment, if the detection entity 113 is a stationary device (e.g., a traffic camera), the detection entity 113 may store contextual information indicating a location at which the detection entity 113 is located, a direction at which a particular sensor (e.g., an image sensor) of the detection entity 113 is facing, or a combination thereof. In one embodiment, the detection entity 113 may be a mobile device (e.g., similar to the UE 101) and may be equipped with any of the aforementioned sensors. Such mobile device may be capable of providing images and information indicating a time, location, and orientation at which the images are acquired.

The communication network 121 of system 100 includes one or more networks such as a data network, a wireless network, a telephony network, or any combination thereof. The data network may be any local area network (LAN), metropolitan area network (MAN), wide area network (WAN), a public data network (e.g., the Internet), short range wireless network, or any other suitable packet-switched network, such as a commercially owned, proprietary packet-switched network, e.g., a proprietary cable or fiber-optic network, and the like, or any combination thereof. In addition, the wireless network may be, for example, a cellular network and may employ various technologies including enhanced data rates for global evolution (EDGE), general packet radio service (GPRS), global system for mobile communications (GSM), Internet protocol multimedia subsystem (IMS), universal mobile telecommunications system (UMTS), etc., as well as any other suitable wireless medium, e.g., worldwide interoperability for microwave access (WiMAX), Long Term Evolution (LTE) networks, 5G networks, code division multiple access (CDMA), wideband code division multiple access (WCDMA), wireless fidelity (Wi-Fi), wireless LAN (WLAN), Bluetooth®, Internet Protocol (IP) data casting, satellite, mobile ad-hoc network (MANET), and the like, or any combination thereof.

The services platform 115 may be an original equipment manufacturer (OEM) platform that provides one or more services 117 a-117 n (collectively referred to as services 117). In one embodiment the one or more service 117 may be sensor data collection services. By way of example, vehicle sensor data provided by the sensors 107 may be transferred to the UE 101, the prediction platform 123, the database 125, or other entities communicatively coupled to the communication network 121 through the service platform 115. The services 117 may also be other third-party services and include mapping services, navigation services, travel planning services, weather-based services, notification services, social networking services, content (e.g., audio, video, images, etc.) provisioning services, application services, storage services, contextual information determination services, location-based services, information-based services, etc. In one embodiment, the services platform 115 uses the output data generated by of the prediction platform 123 to provide services such as navigation, mapping, other location-based services, etc.

In one embodiment, the content providers 119 a-119 n (collectively referred to as content providers 119) may provide content or data (e.g., including geographic data, parametric representations of mapped features, etc.) to the UE 101, the vehicle 105, services platform 115, the vehicle 105, the database 125, the prediction platform 123, or the combination thereof. The content provided may be any type of content, such as map content, textual content, audio content, video content, image content, etc. In one embodiment, the content providers 119 may provide content that may aid in providing a prediction of adverse road locations, and/or other related characteristics. In one embodiment, the content providers 119 may also store content associated with the UE 101, the vehicle 105, services platform 115, the vehicle 105, the database 125, the prediction platform 123, or the combination thereof. In another embodiment, the content providers 119 may manage access to a central repository of data, and offer a consistent, standard interface to data, such as a repository of the database 125.

In the illustrated embodiment, the prediction platform 123 may be a platform with multiple interconnected components. The prediction platform 123 may include multiple servers, intelligent networking devices, computing devices, components and corresponding software for rendering a prediction of adverse road locations. It should be appreciated that that the prediction platform 123 may be a separate entity of the system 100, included within the UE 101 (e.g., as part of the applications 103), included within the vehicle 105 (e.g., as part of an application stored in memory of the on-board computing platform 109), included within the services platform 115 (e.g., as part of an application stored in server memory for the services platform 115), or a combination thereof.

The prediction platform 123 is capable of training a machine learning model based on historical data of a plurality of events in which vehicles are impacted by a tire pressure change and using the trained machine learning model to predict an adverse road location. In one embodiment, the prediction platform 123 defines a tire pressure change as a predetermined rate of tire pressure loss. Each of the events indicates an occurrence in which one or more tires of a vehicle is impacted by a tire pressure change. Each event is associated with: (1) sensor data acquired from one or more sensors of the vehicle; (2) travel data of the vehicle; (3) vehicle attribute data indicating one or more attributes of the vehicle; (4) or a combination thereof.

The sensor data may indicate one or more tire pressure levels of one or more tires of the vehicle over a predetermined period. The predetermined period may define: (1) a total amount of time elapsed for the vehicle to travel a route in which the vehicle was impacted by the tire pressure change; (2) an amount of time less than the total amount but including a period in which the vehicle was impacted by the tire pressure change; (3) the period in which the vehicle was impacted by the tire pressure change; or (4) one or more lesser periods within the period in which the vehicle was impacted by the tire pressure change. The sensor data may represent the one or more tire pressure levels as a function of time (e.g., tire pressure loss per minute, per second, per milli-second, etc.). The sensor data may also indicate: (1) one or more speed levels of the vehicle within the predetermined period; (2) one or more ambient temperature levels of the vehicle within the predetermined period (or ambient temperature levels with respect to a tire of the vehicle); (3) one or more internal temperature levels of one or more tires of the vehicle within the predetermined period; (4) an amount of time elapsed starting from a time point of the latest instalment of a tire to the start of the predetermined period; (5) a total amount of distance travelled by the vehicle; (6) a total amount of distance travelled by the vehicle since the latest instalment of a tire; or (7) a combination thereof. Generally, the sensor data may be acquired from the vehicle; however, certain sensor data may be acquired via one or more detection entities 113 that is proximate to the vehicle during the predetermined period. Such sensor data may be: (1) a speed of the vehicle; (2) a temperature level of an environment in which the vehicle is located; (3) a temperature level of a road pavement on which the vehicle is positioned; (3) an atmospheric pressure level of the environment; or (4) a combination thereof.

The travel data may indicate: (1) one or more road segments in which the vehicle has travelled within the predetermined period; (2) one or more road attributes of the one or more road segments; (3) one or more weather conditions that has impacted the one or more road segment; (4) whether one or more road events (e.g., road works, road accident, etc.) was impacting the one or more road segments; (5) a degree of traffic impacting the one or more road segments; (6) a season in which the predetermined period occurs; (7) a date in which the predetermined period occurs; or (8) a combination thereof. The one or more road attributes may indicate: (1) a type of road; (2) a classification of a road segment; (3) a curvature of the road segment; (4) coefficient of friction associated with the road segment; (5) a composition of the road segment; (6) a slope of the road segment; (7) whether the road segment includes one or more objects, such as a bump, a crack, a pothole, a rail, etc.; or (8) a combination thereof. For each road segment defined by the travel data, the prediction platform 123 identifies point-of-interest (POI) information associated with said road segment. Specifically, the POI information may indicate that a POI is associated with a road segment if the road segment is: (1) proximate to the POI (e.g., within 20 meters of the road segment); (2) is directly connected to a private road segment of the POI; or (3) a combination thereof. The POI information may indicate: (1) a type of POI; (2) a classification of POI; (3) a type of personnel associated with the POI (e.g., occupational data associated with an employee of the POI); (4) a type of vehicle associated with the POI (e.g., a service vehicle associated with the POI); (5) one or more time points in which one or more vehicles associated with the POI enters and/or exists the POI (e.g., scheduled deliveries); (6) hours of operation associated with the POI; (7) other types of data that indicate a function of the POI (e.g., information indicating a type of service provided by the POI); (8) types goods or items manufactured in the POI and/or transported in and out of the POI; or (9) a combination thereof. The POI information may provide contextual information regarding a type of debris that may be present within a road segment that is associated with a POI. For example, a road segment associated with a nail factory may include nails, or a road segment associated with a glass factory may include glass pieces, etc.

The vehicle attribute data may indicate: (1) a size of the vehicle; (2) a weight of the vehicle; (3) a make and/or model of the vehicle; (4) a classification of the vehicle; (5) a type of tire equipped by the vehicle; (6) one or more specifications associated with the vehicle or one or more functions thereof; or (7) a combination thereof. The travel data and the vehicle attribute data may be acquired from the vehicle, one or more detection entities 113 (such as system that can identify vehicles and classify the vehicles to acquire attributes associated with the vehicles), the services platform 115, one or more content providers 119, the database 125, or a combination thereof.

The prediction platform 123 may use the historical data to identify one or more locations in which a plurality of vehicles is impacted by a tire pressure change. Specifically, for each of the plurality of vehicles, the prediction platform 123 identifies a period (i.e., the predetermined period) in which said vehicle is impacted by a tire pressure change. Such period may indicate an event in which said vehicle has traversed a portion of a road segment. The prediction platform 123 may further determine whether one or more additional vehicles has been impacted by a tire pressure change at the portion of the road segment. If a plurality of vehicles has been impacted by a tire pressure change at the portion, the prediction platform 123 may identify the portion as an adverse road location. For each adverse road location, the prediction platform 123 acquires relevant sensor data, travel data, vehicle attribute data associated with one or more vehicles that has traversed the adverse road location. Using the data, the prediction platform 123 may train the machine learning model to predict one or more adverse road locations within a road network. In one embodiment, the prediction platform 123 may use the data to identify one or more map related features (e.g., map attributes, POIs, etc.) that contributes the most to the tire pressure changes that have occurred at an adverse road location. In such embodiment, the prediction platform 123 may further identify contextual features, such as weather conditions, sensor data associated with vehicles impacted by tire pressure changes and/or adverse road locations, traffic conditions, road works, accidents, etc., and identify relationships between such features and the map related features to identify one or more combinations that attributes the most to the tire pressure changes. In one embodiment, the prediction platform 123 may define the most contributing factors for the tire pressure changes based on a number and/or a frequency at which said factors were observed at one or more adverse road locations in which the tire pressure changes have occurred.

In one embodiment, to record an accurate event of a tire pressure change for the historical data, the prediction platform 123 may normalize sensor data acquired by the vehicle impacted by the tire pressure change. Specifically, since road pavement temperature levels, ambient temperature levels, and atmospheric pressure levels impact tire pressure levels, the prediction platform 123 may acquire sensor data associated with the vehicle (such as tire pressure levels) over a period defining the tire pressure change and sensor data associated with one or more road segments (such as road pavement temperature levels, atmospheric temperature levels, atmospheric pressure levels, etc.) over the period and normalize the sensor data associated with the vehicle in view of the sensor data associated with the one or more road segments, thereby causing the historical data to only indicate events of tire pressure changes due to attributes of a road and/or physical objects within the road (e.g., sharp objects within a road, pot holes, etc.).

Once the machine learning is trained, the prediction platform 123 may cause the machine learning model to predict an adverse road location in response to receiving a request. In one embodiment, the request may be transmitted from the vehicle 105. In such embodiment, the request may include information indicating: (1) a route of the vehicle 105; (2) a current road segment in which the vehicle 105 is located; (3) a heading of the vehicle 105; (4) sensor data associated with the vehicle 105; or (5) a combination thereof. Using the information, the prediction platform 123 may identify one or more road segments in which the vehicle 105 is predicted to traverse. For each road segment, the prediction platform 123 acquires map related features and contextual features associated with said road segment from one or more detection entities 113 located within said road segment, the services platform 115, one or more content providers 119, the database 125, or a combination thereof. The prediction platform 123 may input such features of each road segment to the machine learning model, and in response, the machine learning model outputs, for each road segment, a likelihood of which said road segment will induce a tire pressure change for the vehicle 105. If the likelihood for a road segment exceed a threshold, the prediction platform 123 may: (1) cause a notification to the UE 101 and/or a user interface associated with the vehicle 105 indicating the road segment; (2) cause a notification to the UE 101 and/or a user interface associated with the vehicle 105 indicating that the route includes the road segment; (3) generate an alternative route that avoids the road segment and provide the alternative route to the UE 101 and/or a user interface associated with the vehicle 105; (4) cause a notification to an establishment; or (5) a combination thereof. In one embodiment, the prediction platform 123 may input any road segment and features associated therewith to the machine learning model, and in response, the machine learning model may output the likelihood of which said road segment will induce a tire pressure change.

It is contemplated that the accuracy at which the machine learning model renders an accurate prediction is defined, at least in part by, a degree at which attributes of the vehicle 105, the map related features, and the contextual features correspond to the historical data. By way of example, if the vehicle 105 corresponds to a vehicle that was impacted by a tire pressure change, as indicated by the historical data (e.g., similar vehicle types, tire types, etc.), the machine learning model may yield a higher chance of providing an accurate prediction. By way of another example, if a road segment in which the vehicle 105 is predicted to traverse and the condition thereof (e.g., atmospheric pressure and temperature, road pavement temperature, etc.) correspond to an adverse road location and the condition thereof, as indicated by the historical data, the machine learning model may yield a higher chance of providing an accurate prediction. It is further contemplated that the historical data may not be sufficient enough to detail all road segments within a road network. As such, in one embodiment, the prediction platform 123 may adjust sensor data (e.g., atmospheric pressure and temperature of a location, road pavement temperature, etc.) associated with a road segment of which the vehicle 105 is predicted to traverse to increase a likelihood of a match between said sensor data and an event of a tire pressure change, as indicated by the historical data. Specifically, since temperature is proportional to pressure, the prediction platform 123 may adjust the sensor data to yield a match. Similarly, other types of data (e.g., vehicle weight, vehicle speed, tire characteristics, etc.) may be adjusted for the purposes of yielding a match to a corresponding event of a tire pressure change, as indicated by the historical data.

Subsequent to rendering a prediction of an adverse road location, the prediction platform 123 may further train the machine learning model based on the outcome of the prediction. For example, subsequent to rendering the prediction for the vehicle 105, the prediction platform 123 may receive sensor data, such as tire pressure levels associated with the vehicle 105, ambient temperature levels associated with the adverse road location, road pavement temperature associated with the adverse road location, atmospheric pressure level associated with the road location, etc., as the vehicle 105 traverses the adverse road location. If the sensor data indicates a positive observation (i.e., the vehicle 105 is impacted by a tire pressure change), the prediction platform 123 may increase a confidence value associated with the prediction. Conversely, if the sensor data indicates a negative observation (i.e., the vehicle 105 is not impacted by a tire pressure change), the prediction platform 123 may lower the confidence value and update the machine learning model based on the sensor data.

In one embodiment, the prediction platform 123 may store user preference information indicating a configurable threshold at which the prediction platform 123 is triggered to render a prediction of a tire pressure change associated with one or more road segments. For example, a user of the vehicle 105 may input, via the UE 101, a threshold of 5 PSI for a tire pressure drop, and the prediction platform 123 causes the machine learning model to render a prediction of a tire pressure change associated with one or more road segments in response to a tire pressure of the vehicle 105 dropping below the 5 PSI threshold during a course of travel for the vehicle 105.

In one embodiment, the prediction platform 123 may predict one or more road attributes associated with one or more road segments based on historical data of a plurality of events in which tire pressure levels associated with a plurality of vehicles fluctuate over one or more periods. In such embodiment, the historical data is not limited to indicating events in which tire pressure drops have occurred and may further indicate events in which tire pressure levels of one or more vehicles have increased. The prediction platform 123 may use the historical data to train a machine learning model, and once the machine learning model is trained, the machine learning model may output a prediction of one or more road attributes of a road segment as a function of sensor data acquired from a vehicle that is currently traversing or has traversed the road segment. The one or more road attributes may indicate: (1) a type of road; (2) a classification of a road segment; (3) a curvature of the road segment; (4) coefficient of friction associated with the road segment; (5) a composition of the road segment; (6) a slope of the road segment; (7) whether the road segment includes one or more objects, such as a bump, a crack, a pothole, a rail, etc.; or (8) a combination thereof. For example, if the vehicle 105 is traversing a road segment, the prediction platform 123 may acquire sensor data indicating tire pressure levels associated with the vehicle 105 and input the sensor data to the machine learning model. In response, the machine learning model may output a prediction that the vehicle 105 is traversing a paved road segment. The output of the machine learning model may be validated through sensors, such as cameras, and/or user input, and the machine learning model may be further trained based on the validation.

In the illustrated embodiment, the database 125 stores information on road links (e.g., road length, road breadth, slope information, curvature information, etc.) and probe data for one or more road links (e.g., traffic density information). In one embodiment, the database 125 may include any multiple types of information that can provide means for aiding in providing a prediction of adverse road locations. It should be appreciated that the information stored in the database 125 may be acquired from any of the elements within the system 100, other vehicles, sensors, database, or a combination thereof.

In one embodiment, the UE 101, the vehicle 105, the detection entity 113, the services platform 115, the content providers 119, the prediction platform 123 communicate with each other and other components of the communication network 121 using well known, new or still developing protocols. In this context, a protocol includes a set of rules defining how the network nodes within the communication network 121 interact with each other based on information sent over the communication links. The protocols are effective at different layers of operation within each node, from generating and receiving physical signals of various types, to selecting a link for transferring those signals, to the format of information indicated by those signals, to identifying which software application executing on a computer system sends or receives the information. The conceptually different layers of protocols for exchanging information over a network are described in the Open Systems Interconnection (OSI) Reference Model.

Communications between the network nodes are typically affected by exchanging discrete packets of data. Each packet typically comprises (1) header information associated with a particular protocol, and (2) payload information that follows the header information and contains information that may be processed independently of that particular protocol. In some protocols, the packet includes (3) trailer information following the payload and indicating the end of the payload information. The header includes information such as the source of the packet, its destination, the length of the payload, and other properties used by the protocol. Often, the data in the payload for the particular protocol includes a header and payload for a different protocol associated with a different, higher layer of the OSI Reference Model. The header for a particular protocol typically indicates a type for the next protocol contained in its payload. The higher layer protocol is said to be encapsulated in the lower layer protocol. The headers included in a packet traversing multiple heterogeneous networks, such as the Internet, typically include a physical (layer 1) header, a data-link (layer 2) header, an internetwork (layer 3) header and a transport (layer 4) header, and various application (layer 5, layer 6 and layer 7) headers as defined by the OSI Reference Model.

FIG. 2 is a diagram of a database 125 (e.g., a map database), according to one embodiment. In one embodiment, the database 125 includes geographic data 1250 used for (or configured to be compiled to be used for) mapping and/or navigation-related services. In one embodiment, the following terminology applies to the representation of geographic features in the database 125.

a. “Node”—A point that terminates a link.

b. “road/line segment”—A straight line connecting two points.

c. “Link” (or “edge”)—A contiguous, non-branching string of one or more road segments terminating in a node at each end.

In one embodiment, the database 125 follows certain conventions. For example, links do not cross themselves and do not cross each other except at a node. Also, there are no duplicated shape points, nodes, or links. Two links that connect each other have a common node.

As shown, the database 125 includes node data records 1251, road segment or link data records 1253, POI data records 1255, tire pressure records 1257, other records 1259, and indexes 1261, for example. More, fewer or different data records can be provided. In one embodiment, additional data records (not shown) can include cartographic (“carto”) data records, routing data, and maneuver data. In one embodiment, the indexes 1261 may improve the speed of data retrieval operations in the database 125. In one embodiment, the indexes 1261 may be used to quickly locate data without having to search every row in the database 125 every time it is accessed.

In exemplary embodiments, the road segment data records 1253 are links or segments representing roads, streets, or paths, as can be used in the calculated route or recorded route information for determination of one or more personalized routes. The node data records 1251 are end points (such as intersections) corresponding to the respective links or segments of the road segment data records 1253. The road link data records 1253 and the node data records 1251 represent a road network, such as used by vehicles, cars, and/or other entities. Alternatively, the database 125 can contain path segment and node data records or other data that represent pedestrian paths or areas in addition to or instead of the vehicle road record data, for example.

Links, segments, and nodes can be associated with attributes, such as geographic coordinates, a number of road objects (e.g., road markings, road signs, traffic light posts, etc.), types of road objects, traffic directions for one or more portions of the links, segments, and nodes, traffic history associated with the links, segments, and nodes, street names, address ranges, speed limits, turn restrictions at intersections, presence of roadworks, and other navigation related attributes, as well as POIs, such as gasoline stations, hotels, restaurants, museums, stadiums, offices, automobile dealerships, auto repair shops, factories, buildings, stores, parks, etc. The database 125 can include data about the POIs and their respective locations in the POI data records 1255. The data about the POIs may include attribute data associated with the POIs, such as: (1) a type of POI; (2) a classification of POI; (3) a type of personnel associated with the POI (e.g., occupational data associated with an employee of the POI); (4) a type of vehicle associated with the POI (e.g., a service vehicle associated with the POI); (5) one or more time points in which one or more vehicles associated with the POI enters and/or exists the POI (e.g., scheduled deliveries); (6) hours of operation associated with the POI; (7) directories associated with the POI; (8) types goods or items manufactured in the POI and/or transported in and out of the POI; (9) other types of data that indicate a function of the POI (e.g., information indicating a type of service provided by the POI); or (10) a combination thereof. The database 125 can also include data about places, such as cities, towns, or other communities, and other geographic features, such as bodies of water, mountain ranges, etc. Such place or feature data can be part of the POI data records 1255 or can be associated with POIs or POI data records 1255 (such as a data point used for displaying or representing a position of a city).

The tire pressure records 1257 includes historical data of a plurality of events in which vehicles are impacted by a tire pressure change. Each of the events indicates an occurrence in which one or more tires of a vehicle is impacted by a tire pressure change. Each event is associated with: (1) sensor data acquired from one or more sensors of the vehicle; (2) travel data of the vehicle; (3) vehicle attribute data indicating one or more attributes of the vehicle; (4) or a combination thereof. In one embodiment, the historical data may indicate instances in which vehicles traverse a plurality of types of road segments, sensor data associated with the vehicles and the road segment types, travel data associated with the vehicles, and vehicle attribute data of the vehicles. The types of road segments may be defined by: (1) a curvature of the road segment; (2) a number of turns within the road segment; (3) roughness of the road segment; (4) a slope of the road segment; (5) a composition of the road segment; (6) coefficient of friction associated with the road segment; (7) whether the road segment includes one or more objects, such as a bump, a crack, a pothole, a rail, etc.; or (8) a combination thereof.

Other records 1259 may include computer code instructions and/or algorithms for executing a machine learning model that is capable of providing a prediction of adverse road locations ysis. The other records 1259 may further include verification data indicating: (1) whether a verification of a prediction for an adverse road location was conducted; (2) whether the verification validates the prediction; or (3) a combination thereof.

In one embodiment, the database 125 can be maintained by one or more of the content providers 119 in association with a map developer. The map developer can collect geographic data to generate and enhance the database 125. There can be different ways used by the map developer to collect data. These ways can include obtaining data from other sources, such as municipalities or respective geographic authorities. In addition, the map developer can employ field personnel to travel by vehicle along roads throughout the geographic region to observe attributes associated with one or more road segments and/or record information about them, for example. Also, remote sensing, such as aerial or satellite photography, can be used.

The database 125 can be a master database stored in a format that facilitates updating, maintenance, and development. For example, the master database or data in the master database can be in an Oracle spatial format or other spatial format (e.g., accommodating different map layers), such as for development or production purposes. The Oracle spatial format or development/production database can be compiled into a delivery format, such as a geographic data files (GDF) format. The data in the production and/or delivery formats can be compiled or further compiled to form database products or databases, which can be used in end user navigation devices or systems.

For example, geographic data is compiled (such as into a platform specification format (PSF) format) to organize and/or configure the data for performing navigation-related functions and/or services, such as route calculation, route guidance, map display, speed calculation, distance and travel time functions, and other functions, by a navigation device, such as by the vehicle 105, for example. The navigation-related functions can correspond to vehicle navigation, pedestrian navigation, or other types of navigation. The compilation to produce the end user databases can be performed by a party or entity separate from the map developer. For example, a customer of the map developer, such as a navigation device developer or other end user device developer, can perform compilation on a received database in a delivery format to produce one or more compiled navigation databases.

The processes described herein for providing a prediction of adverse road locations may be advantageously implemented via software, hardware (e.g., general processor, Digital Signal Processing (DSP) chip, an Application Specific Integrated Circuit (ASIC), Field Programmable Gate Arrays (FPGAs), etc.), firmware or a combination thereof. Such exemplary hardware for performing the described functions is detailed below.

FIG. 3 is a diagram of the components of the prediction platform 123, according to one embodiment. By way of example, the prediction platform 123 includes one or more components for providing a prediction of adverse road locations. It is contemplated that the functions of these components may be combined in one or more components or performed by other components of equivalent functionality. In this embodiment, the prediction platform 123 includes a detection module 301, a calculation module 303, a notification module 305, and a presentation module 307.

The detection module 301 is capable of acquiring data from the UE 101, the vehicle 105 (or one or more other vehicles similar to the vehicle 105), one or more detection entities 113, services platform 115, content providers 119, the database 125, or a combination thereof to provide a prediction of adverse road locations. The data may be: (1) sensor data associated with a tire pressure change; (2) travel data of a vehicle impacted by the tire pressure change; (3) vehicle attribute data indicating one or more attributes of the vehicle impacted by the tire pressure change; (4) or a combination thereof. The detection module 301 may also receive a request for rendering a prediction of an adverse road location. Such request may be received via the UE 101 or a user equipment associated with the vehicle 105. The request may include information indicating: (1) a route of the vehicle 105; (2) a current road segment in which the vehicle 105 is located; (3) a heading of the vehicle 105; (4) sensor data associated with the vehicle 105; or (5) a combination thereof. The detection module 301 may also acquire map related features (e.g., map attributes, POIs, etc.) and contextual features (e.g., such as weather conditions, sensor data associated with vehicles impacted by tire pressure changes and/or adverse road locations, traffic conditions, road works, accidents, etc.) associated with one or more road segments. Contextual features that indicate sensor data may indicate tire temperature levels and tire pressure levels of the vehicle 105, environmental temperature levels of the one or more road segments, road pavement temperature levels of the one or more road segments, atmospheric pressure levels of the one or more road segments, or a combination thereof.

The calculation module 303 uses the data acquired by the detection module 301 to render a prediction of one or more adverse road location. In one embodiment, the calculation module 303 uses the historical data to identify one or more locations in which a plurality of vehicles is impacted by a tire pressure change. Specifically, for each of the plurality of vehicles, the calculation module 303 identifies a period in which said vehicle is impacted by a tire pressure change. Such period may indicate an event in which said vehicle has traversed a portion of a road. The calculation module 303 may further determine whether one or more additional vehicles has been impacted by a tire pressure change at the portion of the road. If a plurality of vehicles has been impacted by a tire pressure change at the portion, the calculation module 303 may identify the portion as an adverse road location. For each adverse road location, the calculation module 303 acquires relevant sensor data, travel data, vehicle attribute data associated with one or more vehicles that has traversed the adverse road location. For example, FIG. 4 , illustrates an example adverse road location and historical data of tire pressure changes associated thereto. In the illustrated embodiment, a road segment 401 within a geographic location 400 is associated with historical data 403 of a plurality of events indicating tire pressure changes. Each of the plurality of events indicates a road segment in which a vehicle is impacted by a tire pressure change, temporal information associated with the tire pressure change, vehicle sensor data associated with the tire pressure change, attributes of the road segment, and vehicle attribute data associated with the vehicle. Using the sensor data, the travel data, and the vehicle attribute data, the calculation module 303 may train the machine learning model to predict one or more adverse road locations within a road network. In one embodiment, the machine learning model may be a random forest, a logistic, a decision trees, neural networks, or a combination thereof. In one embodiment, the calculation module 303 may use the data to identify one or more map related features (e.g., map attributes, POIs, etc.) that contributes the most to the tire pressure changes that have occurred at an adverse road location. In such embodiment, the calculation module 303 may further identify contextual features, such as weather conditions, sensor data associated with vehicles impacted by tire pressure changes and/or adverse road locations, traffic conditions, road works, accidents, etc., and identify relationships between such features and the map related features to identify one or more combinations that attributes the most to the tire pressure changes. In one embodiment, the calculation module 303 may define the most contributing factors for the tire pressure changes based on a number and/or a frequency at which said factors were observed at one or more adverse road locations in which the tire pressure changes have occurred.

In one embodiment, the calculation module 303 may normalize sensor data acquired by the vehicle impacted by the tire pressure change. Specifically, since road pavement temperature levels, ambient temperature levels, and atmospheric pressure levels impact tire pressure levels, the calculation module 303 may acquires sensor data associated with the vehicle (such as tire pressure levels) over a period defining the tire pressure change and sensor data associated with one or more road segments (such as road pavement temperature levels, atmospheric temperature levels, atmospheric pressure levels, etc.) over the period and normalizes the sensor data associated with the vehicle in view of the sensor data associated with the one or more road segments, thereby causing the historical data to only indicate events of tire pressure changes due to attributes of a road and/or physical objects within the road (e.g., sharp objects within a road, pot holes, etc.).

Once the machine learning is trained, the calculation module 303 may cause the machine learning model to predict an adverse road location in response to receiving a request via the detection module 301. In one embodiment, the request may be transmitted from the vehicle 105. In such embodiment, the request may include information indicating: (1) a route of the vehicle 105; (2) a current road segment in which the vehicle 105 is located; (3) a heading of the vehicle 105; (4) sensor data associated with the vehicle 105; or (5) a combination thereof. Using the information, the calculation module 303 may identify one or more road segments in which the vehicle 105 is predicted to traverse. For each road segment, the calculation module 303 acquires map related features and contextual features associated with said road segment via the detection module 301. The calculation module 303 may input such features of each road segment to the machine learning model, and in response, the machine learning model outputs, for each road segment, a likelihood of which said road segment will induce a tire pressure change for the vehicle 105. In one embodiment, the calculation module 303 may input any road segment and features associated therewith to the machine learning model, and in response, the machine learning model may output the likelihood of which said road segment will induce a tire pressure change.

In one embodiment, the calculation module 303 may adjust sensor data (e.g., atmospheric pressure and temperature of a location, road pavement temperature, etc.) associated with a road segment of which the vehicle 105 is predicted to traverse to increase a likelihood of a match between said sensor data and an event of a tire pressure change, as indicated by the historical data. Specifically, since temperature is proportional to pressure, the calculation module 303 may adjust the sensor data to yield a match. Similarly, other types of data (e.g., vehicle weight, vehicle speed, tire characteristics, etc.) may be adjusted for the purposes of yielding a match of a corresponding event of a tire pressure change, as indicated by the historical data.

Subsequent to rendering a prediction of an adverse road location, the calculation module 303 may further train the machine learning model based on the outcome of the prediction. For example, subsequent to rendering the prediction for the vehicle 105, the calculation module 303 may receive sensor data, such as tire pressure levels associated with the vehicle 105, ambient temperature levels associated with the adverse road location, road pavement temperature associated with the adverse road location, atmospheric pressure level associated with the road location, etc., as the vehicle 105 traverses the adverse road location. If the sensor data indicates a positive observation (i.e., the vehicle 105 is impacted by a tire pressure change), the calculation module 303 may increase a confidence value associated with the prediction. Conversely, if the sensor data indicates a negative observation (i.e., the vehicle 105 is not impacted by a tire pressure change), the calculation module 303 may lower the confidence value and update the machine learning model based on the sensor data.

In one embodiment, the calculation module 303 may receive user preference information indicating a configurable threshold at which the calculation module 303 is triggered to render a prediction of a tire pressure change associated with one or more road segments. For example, a user of the vehicle 105 may input, via the UE 101, a threshold of 5 PSI for a tire pressure drop, and the calculation module 303 causes the machine learning model to render a prediction of a tire pressure change associated with one or more road segments in response to a tire pressure of the vehicle 105 dropping below the 5 PSI threshold during a course of travel for the vehicle 105.

In one embodiment, the calculation module 303 may predict one or more road attributes associated with one or more road segments based on historical data of a plurality of events in which tire pressure levels associated with a plurality of vehicles fluctuate over one or more periods. In such embodiment, the historical data is not limited to indicating events in which tire pressure drops have occurred and may further indicate events in which tire pressure levels of one or more vehicles have increased. The calculation module 303 may use the historical data to train a machine learning model, and once the machine learning model is trained, the machine learning model may output a prediction of one or more road attributes of a road segment as a function of sensor data acquired from a vehicle that is currently traversing or has traversed the road segment. The one or more road attributes may indicate: (1) a type of road; (2) a classification of a road segment; (3) a curvature of the road segment; (4) coefficient of friction associated with the road segment; (5) a composition of the road segment; (6) a slope of the road segment; (7) whether the road segment includes one or more objects, such as a bump, a crack, a pothole, a rail, etc.; or (8) a combination thereof. For example, if the vehicle 105 is traversing a road segment, the calculation module 303 may acquire sensor data indicating tire pressure levels associated with the vehicle 105 and input the sensor data to the machine learning model. In response, the machine learning model may output a prediction that the vehicle 105 is traversing a paved road segment. The output of the machine learning model may be validated through sensors, such as cameras, and/or user input, and the machine learning model may be further trained based on the validation.

The notification module 305 may generate a notification associated with providing a prediction of adverse road locations. The notification module 305 may cause the notification to the UE 101 and/or one or more other UEs associated with the vehicle 105. In such embodiment, the notification may indicate: (1) a likelihood of which a road location induces a tire pressure change; (2) an alternative route that avoids one or more adverse road locations; (3) a confidence associated with a prediction of adverse road locations; (4) historical data associated with tire pressure changes at one or more road locations; or (5) a combination thereof. The notification may include sound notification, display notification, vibration, or a combination thereof. In one embodiment, the notification module 305 may provide the notification to a local municipality/establishment.

The presentation module 307 obtains a set of information, data, and/or calculated results from other modules, and continues with providing a presentation of a visual representation to the UE 101 and/or any other user interface associated with the vehicle 105. The visual representation may indicate any of the information presented by the notification module 305. FIG. 5 illustrates an example visual representation 500 indicating an adverse road location. The first visual representation 500 depicts a scenario in which a vehicle 501 is traversing a route 503 to a destination 505, and the calculation module 303 has rendered a prediction that a road segment 507 of the route 503 is likely to induce a tire pressure drop for the vehicle 501 (i.e., the adverse road location). In response, the presentation module 307 displays a message 509 stating “TIRE PRESSURE DROP IS LIKELY TO OCCUR IN THIS AREA. FIND ALTERNATIVE ROUTE?” In one embodiment, a visual representation may further include an alternative route that avoids an adverse road location.

The above presented modules and components of the prediction platform 123 can be implemented in hardware, firmware, software, or a combination thereof. Though depicted as a separate entity in FIG. 3 , it is contemplated that the prediction platform 123 may be implemented for direct operation by the UE 101, the vehicle 105, the services platform 115, one or more of the content providers 119, or a combination thereof. As such, the prediction platform 123 may generate direct signal inputs by way of the operating system of the UE 101, the vehicle 105, the services platform 115, the one or more of the content providers 119, of the combination thereof for interacting with the applications 103. The various executions presented herein contemplate any and all arrangements and models.

FIG. 6 is a flowchart of a process 600 for training a machine learning model to predict a likelihood in which a road location induces a tire pressure change, according to one embodiment. In one embodiment, the prediction platform 123 performs the process 600 and is implemented in, for instance, a chip set including a processor and a memory as shown in FIG. 9 .

In step 601, the prediction platform 123 receives historical data of a plurality of events in which vehicles are impacted by a tire pressure change and uses the trained machine learning model to predict an adverse road location. Each of the events indicates an occurrence in which one or more tires of a vehicle is impacted by a tire pressure change. Each event is associated with: (1) sensor data acquired from one or more sensors of the vehicle; (2) travel data of the vehicle; (3) vehicle attribute data indicating one or more attributes of the vehicle; (4) or a combination thereof. The sensor data may indicate one or more tire pressure levels of one or more tires of a vehicle, temperature levels associated with one or more road segments on which the vehicle traversed, and/or atmospheric pressure levels associated with the one or more road segments on which the vehicle traversed. The travel data may indicate: (1) one or more road segments in which the vehicle has travelled within the predetermined period; (2) one or more road attributes of the one or more road segments; (3) one or more weather conditions that has impacted the one or more road segment; (4) whether one or more road events (e.g., road works, road accident, etc.) was impacting the one or more road segments; (5) a degree of traffic impacting the one or more road segments; (6) a season in which the predetermined period occurs; (7) a date in which the predetermined period occurs; or (8) a combination thereof. Each road segment defined by the travel data may be associated with POI information. The POI information may indicate that a POI is associated with a road segment if the road segment is: (1) proximate to the POI (e.g., within 20 meters of the road segment); (2) is directly connected to a private road segment of the POI; or (3) a combination thereof. The POI information may indicate: (1) a type of POI; (2) a classification of POI; (3) a type of personnel associated with the POI (e.g., occupational data associated with an employee of the POI); (4) a type of vehicle associated with the POI (e.g., a service vehicle associated with the POI); (5) one or more time points in which one or more vehicles associated with the POI enters and/or exists the POI (e.g., scheduled deliveries); (6) hours of operation associated with the POI; (7) other types of data that indicate a function of the POI (e.g., information indicating a type of service provided by the POI); (8) types goods or items manufactured in the POI and/or transported in and out of the POI; or (9) a combination thereof. The vehicle attribute data may indicate: (1) a size of the vehicle; (2) a weight of the vehicle; (3) a make and/or model of the vehicle; (4) a classification of the vehicle; (5) a type of tire equipped by the vehicle; (6) one or more specifications associated with the vehicle or one or more functions thereof; or (7) a combination thereof.

In step 603, the prediction platform 123 trains a machine learning model to predict adverse road locations by using the historical data. Specifically, the prediction platform 123 identifies one or more road locations in which a plurality of vehicles is impacted by a tire pressure change. For each of the plurality of vehicles, the prediction platform 123 identifies a period in which said vehicle is impacted by a tire pressure change. Such period may indicate an event in which said vehicle has traversed a portion of a road segment. The prediction platform 123 may further determine whether one or more additional vehicles has been impacted by a tire pressure change at the portion of the road segment. If a plurality of vehicles has been impacted by a tire pressure change at the portion, the prediction platform 123 may identify the portion as an adverse road location. For each adverse road location, the prediction platform 123 acquires relevant sensor data, travel data, vehicle attribute data associated with one or more vehicles that has traversed the adverse road location. Using the data, the prediction platform 123 may train the machine learning model to predict one or more adverse road locations within a road network.

FIG. 7 is a flowchart of a process 700 for causing a notification indicating a likelihood of a tire pressure change occurring within a road location. In one embodiment, the prediction platform 123 performs the process 700 and is implemented in, for instance, a chip set including a processor and a memory as shown in FIG. 9 .

In step 701, the prediction platform 123 causes a machine learning model to determine a likelihood in which a road segment induces a tire pressure change for a vehicle. In one embodiment, the prediction platform 123 may cause the machine learning model to determine the likelihood in response receiving a request. The request may include information indicating: (1) a route of the vehicle; (2) a current road segment in which the vehicle is located; (3) a heading of the vehicle; (4) sensor data associated with the vehicle; or (5) a combination thereof. Using the information, the prediction platform 123 may identify one or more road segments in which the vehicle is predicted to traverse. For each road segment, the prediction platform 123 acquires map related features and contextual features associated with said road segment. The prediction platform 123 may input such features of each road segment to the machine learning model, and in response, the machine learning model outputs, for each road segment, a likelihood of which said road segment will induce a tire pressure change for the vehicle.

In step 703, the prediction platform 123 causes a notification indicating the likelihood on a user equipment associated with the vehicle. In one embodiment, the prediction platform 123 may cause such notification if the likelihood exceeds a threshold. In one embodiment, the notification may indicate an alternate route to a destination that avoids the road segment that is associated with the likelihood.

The system, apparatus, and methods described herein enable a map-based server/platform to predict road locations that are likely to induce tire pressure drops for vehicle, thereby ensuring longevity of tires, wheels, and components associated thereto. Further, since the system, apparatus, and methods described herein enables a machine learning model to predict a likelihood of a tire pressure drop occurring in a road location based on features of past events of tire pressure drops that have occurred in other road locations, the application of the machine learning model can be used in road locations that do not have any past records tire pressure drop that have occurred therein.

The processes described herein may be advantageously implemented via software, hardware, firmware or a combination of software and/or firmware and/or hardware. For example, the processes described herein, may be advantageously implemented via processor(s), Digital Signal Processing (DSP) chip, an Application Specific Integrated Circuit (ASIC), Field Programmable Gate Arrays (FPGAs), etc. Such exemplary hardware for performing the described functions is detailed below.

FIG. 8 illustrates a computer system 800 upon which an embodiment may be implemented. Although computer system 800 is depicted with respect to a particular device or equipment, it is contemplated that other devices or equipment (e.g., network elements, servers, etc.) within FIG. 8 can deploy the illustrated hardware and components of system 800. Computer system 800 is programmed (e.g., via computer program code or instructions) to providing a prediction of adverse road locations, as described herein and includes a communication mechanism such as a bus 810 for passing information between other internal and external components of the computer system 800. Information (also called data) is represented as a physical expression of a measurable phenomenon, typically electric voltages, but including, in other embodiments, such phenomena as magnetic, electromagnetic, pressure, chemical, biological, molecular, atomic, sub-atomic and quantum interactions. For example, north and south magnetic fields, or a zero and non-zero electric voltage, represent two states (0, 1) of a binary digit (bit). Other phenomena can represent digits of a higher base. A superposition of multiple simultaneous quantum states before measurement represents a quantum bit (qubit). A sequence of one or more digits constitutes digital data that is used to represent a number or code for a character. In some embodiments, information called analog data is represented by a near continuum of measurable values within a particular range. Computer system 800, or a portion thereof, constitutes a means for providing a prediction of adverse road locations.

A bus 810 includes one or more parallel conductors of information so that information is transferred quickly among devices coupled to the bus 810. One or more processors 802 for processing information are coupled with the bus 810.

A processor (or multiple processors) 802 performs a set of operations on information as specified by computer program code related to providing a prediction of adverse road locations. The computer program code is a set of instructions or statements providing instructions for the operation of the processor and/or the computer system to perform specified functions. The code, for example, may be written in a computer programming language that is compiled into a native instruction set of the processor. The code may also be written directly using the native instruction set (e.g., machine language). The set of operations include bringing information in from the bus 810 and placing information on the bus 810. The set of operations also typically include comparing two or more units of information, shifting positions of units of information, and combining two or more units of information, such as by addition or multiplication or logical operations like OR, exclusive OR (XOR), and AND. Each operation of the set of operations that can be performed by the processor is represented to the processor by information called instructions, such as an operation code of one or more digits. A sequence of operations to be executed by the processor 802, such as a sequence of operation codes, constitute processor instructions, also called computer system instructions or, simply, computer instructions. Processors may be implemented as mechanical, electrical, magnetic, optical, chemical, or quantum components, among others, alone or in combination.

Computer system 800 also includes a memory 804 coupled to bus 810. The memory 804, such as a random access memory (RAM) or any other dynamic storage device, stores information including processor instructions for providing a prediction of adverse road locations. Dynamic memory allows information stored therein to be changed by the computer system 800. RAM allows a unit of information stored at a location called a memory address to be stored and retrieved independently of information at neighboring addresses. The memory 804 is also used by the processor 802 to store temporary values during execution of processor instructions. The computer system 800 also includes a read only memory (ROM) 806 or any other static storage device coupled to the bus 810 for storing static information, including instructions, that is not changed by the computer system 800. Some memory is composed of volatile storage that loses the information stored thereon when power is lost. Also coupled to bus 810 is a non-volatile (persistent) storage device 808, such as a magnetic disk, optical disk or flash card, for storing information, including instructions, that persists even when the computer system 800 is turned off or otherwise loses power.

Information, including instructions for providing a prediction of adverse road locations, is provided to the bus 810 for use by the processor from an external input device 812, such as a keyboard containing alphanumeric keys operated by a human user, a microphone, an Infrared (IR) remote control, a joystick, a game pad, a stylus pen, a touch screen, or a sensor. A sensor detects conditions in its vicinity and transforms those detections into physical expression compatible with the measurable phenomenon used to represent information in computer system 800. Other external devices coupled to bus 810, used primarily for interacting with humans, include a display device 814, such as a cathode ray tube (CRT), a liquid crystal display (LCD), a light emitting diode (LED) display, an organic LED (OLED) display, a plasma screen, or a printer for presenting text or images, and a pointing device 816, such as a mouse, a trackball, cursor direction keys, or a motion sensor, for controlling a position of a small cursor image presented on the display 814 and issuing commands associated with graphical elements presented on the display 814, and one or more camera sensors 894 for capturing, recording and causing to store one or more still and/or moving images (e.g., videos, movies, etc.) which also may comprise audio recordings. In some embodiments, for example, in embodiments in which the computer system 800 performs all functions automatically without human input, one or more of external input device 812, display device 814 and pointing device 816 may be omitted.

In the illustrated embodiment, special purpose hardware, such as an application specific integrated circuit (ASIC) 820, is coupled to bus 810. The special purpose hardware is configured to perform operations not performed by processor 802 quickly enough for special purposes. Examples of ASICs include graphics accelerator cards for generating images for display 814, cryptographic boards for encrypting and decrypting messages sent over a network, speech recognition, and interfaces to special external devices, such as robotic arms and medical scanning equipment that repeatedly perform some complex sequence of operations that are more efficiently implemented in hardware.

Computer system 800 also includes one or more instances of a communications interface 870 coupled to bus 810. Communication interface 870 provides a one-way or two-way communication coupling to a variety of external devices that operate with their own processors, such as printers, scanners and external disks. In general, the coupling is with a network link 878 that is connected to a local network 880 to which a variety of external devices with their own processors are connected. For example, communication interface 870 may be a parallel port or a serial port or a universal serial bus (USB) port on a personal computer. In some embodiments, communications interface 870 is an integrated services digital network (ISDN) card or a digital subscriber line (DSL) card or a telephone modem that provides an information communication connection to a corresponding type of telephone line. In some embodiments, a communication interface 870 is a cable modem that converts signals on bus 810 into signals for a communication connection over a coaxial cable or into optical signals for a communication connection over a fiber optic cable. As another example, communications interface 870 may be a local area network (LAN) card to provide a data communication connection to a compatible LAN, such as Ethernet. Wireless links may also be implemented. For wireless links, the communications interface 870 sends or receives or both sends and receives electrical, acoustic or electromagnetic signals, including infrared and optical signals, that carry information streams, such as digital data. For example, in wireless handheld devices, such as mobile telephones like cell phones, the communications interface 870 includes a radio band electromagnetic transmitter and receiver called a radio transceiver.

The term “computer-readable medium” as used herein refers to any medium that participates in providing information to processor 802, including instructions for execution. Such a medium may take many forms, including, but not limited to computer-readable storage medium (e.g., non-volatile media, volatile media), and transmission media. Non-transitory media, such as non-volatile media, include, for example, optical or magnetic disks, such as storage device 808. Volatile media include, for example, dynamic memory 804. Transmission media include, for example, twisted pair cables, coaxial cables, copper wire, fiber optic cables, and carrier waves that travel through space without wires or cables, such as acoustic waves and electromagnetic waves, including radio, optical and infrared waves. Signals include man-made transient variations in amplitude, frequency, phase, polarization or other physical properties transmitted through the transmission media. Common forms of computer-readable media include, for example, a floppy disk, a flexible disk, hard disk, magnetic tape, any other magnetic medium, a CD-ROM, CDRW, DVD, any other optical medium, punch cards, paper tape, optical mark sheets, any other physical medium with patterns of holes or other optically recognizable indicia, a RAM, a PROM, an EPROM, a FLASH-EPROM, an EEPROM, a flash memory, any other memory chip or cartridge, a carrier wave, or any other medium from which a computer can read. The term computer-readable storage medium is used herein to refer to any computer-readable medium except transmission media.

Logic encoded in one or more tangible media includes one or both of processor instructions on a computer-readable storage media and special purpose hardware, such as ASIC 820.

Network link 878 typically provides information communication using transmission media through one or more networks to other devices that use or process the information. For example, network link 878 may provide a connection through local network 880 to a host computer 882 or to equipment 884 operated by an Internet Service Provider (ISP). ISP equipment 884 in turn provides data communication services through the public, world-wide packet-switching communication network of networks now commonly referred to as the Internet 890.

A computer called a server host 882 connected to the Internet hosts a process that provides a service in response to information received over the Internet. For example, server host 882 hosts a process that provides information representing video data for presentation at display 814. It is contemplated that the components of system 800 can be deployed in various configurations within other computer systems, e.g., host 882 and server 892.

At least some embodiments of the invention are related to the use of computer system 800 for implementing some or all of the techniques described herein. According to one embodiment of the invention, those techniques are performed by computer system 800 in response to processor 802 executing one or more sequences of one or more processor instructions contained in memory 804. Such instructions, also called computer instructions, software and program code, may be read into memory 804 from another computer-readable medium such as storage device 808 or network link 878. Execution of the sequences of instructions contained in memory 804 causes processor 802 to perform one or more of the method steps described herein. In alternative embodiments, hardware, such as ASIC 820, may be used in place of or in combination with software to implement the invention. Thus, embodiments of the invention are not limited to any specific combination of hardware and software, unless otherwise explicitly stated herein.

The signals transmitted over network link 878 and other networks through communications interface 870, carry information to and from computer system 800. Computer system 800 can send and receive information, including program code, through the networks 880 among others, through network link 878 and communications interface 870. In an example using the Internet 890, a server host 882 transmits program code for a particular application, requested by a message sent from computer 800, through Internet 890, ISP equipment 884, local network 880 and communications interface 870. The received code may be executed by processor 802 as it is received or may be stored in memory 804 or in storage device 808 or any other non-volatile storage for later execution, or both. In this manner, computer system 800 may obtain application program code in the form of signals on a carrier wave.

Various forms of computer readable media may be involved in carrying one or more sequence of instructions or data or both to processor 802 for execution. For example, instructions and data may initially be carried on a magnetic disk of a remote computer such as host 882. The remote computer loads the instructions and data into its dynamic memory and sends the instructions and data over a telephone line using a modem. A modem local to the computer system 800 receives the instructions and data on a telephone line and uses an infra-red transmitter to convert the instructions and data to a signal on an infra-red carrier wave serving as the network link 878. An infrared detector serving as communications interface 870 receives the instructions and data carried in the infrared signal and places information representing the instructions and data onto bus 810. Bus 810 carries the information to memory 804 from which processor 802 retrieves and executes the instructions using some of the data sent with the instructions. The instructions and data received in memory 804 may optionally be stored on storage device 808, either before or after execution by the processor 802.

FIG. 9 illustrates a chip set or chip 900 upon which an embodiment may be implemented. Chip set 900 is programmed to provide a prediction of adverse road locations, as described herein, and includes, for instance, the processor and memory components described with respect to FIG. 9 incorporated in one or more physical packages (e.g., chips). By way of example, a physical package includes an arrangement of one or more materials, components, and/or wires on a structural assembly (e.g., a baseboard) to provide one or more characteristics such as physical strength, conservation of size, and/or limitation of electrical interaction. It is contemplated that in certain embodiments the chip set 900 can be implemented in a single chip. It is further contemplated that in certain embodiments the chip set or chip 900 can be implemented as a single “system on a chip.” It is further contemplated that in certain embodiments a separate ASIC would not be used, for example, and that all relevant functions as disclosed herein would be performed by a processor or processors. Chip set or chip 900, or a portion thereof, constitutes a means for performing one or more steps of providing user interface navigation information associated with the availability of functions. Chip set or chip 900, or a portion thereof, constitutes a means for providing a prediction of adverse road locations.

In one embodiment, the chip set or chip 900 includes a communication mechanism such as a bus 901 for passing information among the components of the chip set 900. A processor 903 has connectivity to the bus 901 to execute instructions and process information stored in, for example, a memory 905. The processor 903 may include one or more processing cores with each core configured to perform independently. A multi-core processor enables multiprocessing within a single physical package. Examples of a multi-core processor include two, four, eight, or greater numbers of processing cores. Alternatively or in addition, the processor 903 may include one or more microprocessors configured in tandem via the bus 901 to enable independent execution of instructions, pipelining, and multithreading. The processor 903 may also be accompanied with one or more specialized components to perform certain processing functions and tasks such as one or more digital signal processors (DSP) 907, or one or more application-specific integrated circuits (ASIC) 909. A DSP 907 typically is configured to process real-world signals (e.g., sound) in real time independently of the processor 903. Similarly, an ASIC 909 can be configured to performed specialized functions not easily performed by a more general purpose processor. Other specialized components to aid in performing the inventive functions described herein may include one or more field programmable gate arrays (FPGA), one or more controllers, or one or more other special-purpose computer chips.

In one embodiment, the chip set or chip 900 includes merely one or more processors and some software and/or firmware supporting and/or relating to and/or for the one or more processors. The processor 903 and accompanying components have connectivity to the memory 905 via the bus 901. The memory 905 includes both dynamic memory (e.g., RAM, magnetic disk, writable optical disk, etc.) and static memory (e.g., ROM, CD-ROM, etc.) for storing executable instructions that when executed perform the inventive steps described herein to provide a prediction of adverse road locations. The memory 905 also stores the data associated with or generated by the execution of the inventive steps.

FIG. 10 is a diagram of exemplary components of a mobile terminal 1001 (e.g., a mobile device or vehicle or part thereof) for communications, which is capable of operating in the system of FIG. 1 , according to one embodiment. In some embodiments, mobile terminal 1001, or a portion thereof, constitutes a means for providing a prediction of adverse road locations. Generally, a radio receiver is often defined in terms of front-end and back-end characteristics. The front-end of the receiver encompasses all of the Radio Frequency (RF) circuitry whereas the back-end encompasses all of the base-band processing circuitry. As used in this application, the term “circuitry” refers to both: (1) hardware-only implementations (such as implementations in only analog and/or digital circuitry), and (2) to combinations of circuitry and software (and/or firmware) (such as, if applicable to the particular context, to a combination of processor(s), including digital signal processor(s), software, and memory(ies) that work together to cause an apparatus, such as a mobile phone or server, to perform various functions). This definition of “circuitry” applies to all uses of this term in this application, including in any claims. As a further example, as used in this application and if applicable to the particular context, the term “circuitry” would also cover an implementation of merely a processor (or multiple processors) and its (or their) accompanying software/or firmware. The term “circuitry” would also cover, if applicable, to the particular context, for example, a baseband integrated circuit or applications processor integrated circuit in a mobile phone or a similar integrated circuit in a cellular network device or other network devices.

Pertinent internal components of the telephone include a Main Control Unit (MCU) 1003, a Digital Signal Processor (DSP) 1005, and a receiver/transmitter unit including a microphone gain control unit and a speaker gain control unit. A main display unit 1007 provides a display to the user in support of various applications and mobile terminal functions that perform or support the steps of providing a prediction of adverse road locations. The display 1007 includes display circuitry configured to display at least a portion of a user interface of the mobile terminal (e.g., mobile telephone). Additionally, the display 1007 and display circuitry are configured to facilitate user control of at least some functions of the mobile terminal. An audio function circuitry 1009 includes a microphone 1011 and microphone amplifier that amplifies the speech signal output from the microphone 1011. The amplified speech signal output from the microphone 1011 is fed to a coder/decoder (CODEC) 1013.

A radio section 1015 amplifies power and converts frequency in order to communicate with a base station, which is included in a mobile communication system, via antenna 1017. The power amplifier (PA) 1019 and the transmitter/modulation circuitry are operationally responsive to the MCU 1003, with an output from the PA 1019 coupled to the duplexer 1021 or circulator or antenna switch, as known in the art. The PA 1019 also couples to a battery interface and power control unit 1020.

In use, a user of mobile terminal 1001 speaks into the microphone 1011 and his or her voice along with any detected background noise is converted into an analog voltage. The analog voltage is then converted into a digital signal through the Analog to Digital Converter (ADC) 1023. The control unit 1003 routes the digital signal into the DSP 1005 for processing therein, such as speech encoding, channel encoding, encrypting, and interleaving. In one embodiment, the processed voice signals are encoded, by units not separately shown, using a cellular transmission protocol such as enhanced data rates for global evolution (EDGE), general packet radio service (GPRS), global system for mobile communications (GSM), Internet protocol multimedia subsystem (IMS), universal mobile telecommunications system (UMTS), etc., as well as any other suitable wireless medium, e.g., microwave access (WiMAX), Long Term Evolution (LTE) networks, 5G networks, code division multiple access (CDMA), wideband code division multiple access (WCDMA), wireless fidelity (WiFi), satellite, and the like, or any combination thereof.

The encoded signals are then routed to an equalizer 1025 for compensation of any frequency-dependent impairments that occur during transmission though the air such as phase and amplitude distortion. After equalizing the bit stream, the modulator 1027 combines the signal with a RF signal generated in the RF interface 1029. The modulator 1027 generates a sine wave by way of frequency or phase modulation. In order to prepare the signal for transmission, an up-converter 1031 combines the sine wave output from the modulator 1027 with another sine wave generated by a synthesizer 1033 to achieve the desired frequency of transmission. The signal is then sent through a PA 1019 to increase the signal to an appropriate power level. In practical systems, the PA 1019 acts as a variable gain amplifier whose gain is controlled by the DSP 1005 from information received from a network base station. The signal is then filtered within the duplexer 1021 and optionally sent to an antenna coupler 1035 to match impedances to provide maximum power transfer. Finally, the signal is transmitted via antenna 1017 to a local base station. An automatic gain control (AGC) can be supplied to control the gain of the final stages of the receiver. The signals may be forwarded from there to a remote telephone which may be another cellular telephone, any other mobile phone or a land-line connected to a Public Switched Telephone Network (PSTN), or other telephony networks.

Voice signals transmitted to the mobile terminal 1001 are received via antenna 1017 and immediately amplified by a low noise amplifier (LNA) 1037. A down-converter 1039 lowers the carrier frequency while the demodulator 1041 strips away the RF leaving only a digital bit stream. The signal then goes through the equalizer 1025 and is processed by the DSP 1005. A Digital to Analog Converter (DAC) 1043 converts the signal and the resulting output is transmitted to the user through the speaker 1045, all under control of a Main Control Unit (MCU) 1003 which can be implemented as a Central Processing Unit (CPU).

The MCU 1003 receives various signals including input signals from the keyboard 1047. The keyboard 1047 and/or the MCU 1003 in combination with other user input components (e.g., the microphone 1011) comprise a user interface circuitry for managing user input. The MCU 1003 runs a user interface software to facilitate user control of at least some functions of the mobile terminal 1001 to provide a prediction of adverse road locations. The MCU 1003 also delivers a display command and a switch command to the display 1007 and to the speech output switching controller, respectively. Further, the MCU 1003 exchanges information with the DSP 1005 and can access an optionally incorporated SIM card 1049 and a memory 1061. In addition, the MCU 1003 executes various control functions required of the terminal. The DSP 1005 may, depending upon the implementation, perform any of a variety of conventional digital processing functions on the voice signals. Additionally, DSP 1005 determines the background noise level of the local environment from the signals detected by microphone 1011 and sets the gain of microphone 1011 to a level selected to compensate for the natural tendency of the user of the mobile terminal 1001.

The CODEC 1013 includes the ADC 1023 and DAC 1043. The memory 1061 stores various data including call incoming tone data and is capable of storing other data including music data received via, e.g., the global Internet. The software module could reside in RAM memory, flash memory, registers, or any other form of writable storage medium known in the art. The memory device 1061 may be, but not limited to, a single memory, CD, DVD, ROM, RAM, EEPROM, optical storage, magnetic disk storage, flash memory storage, or any other non-volatile storage medium capable of storing digital data.

An optionally incorporated SIM card 1049 carries, for instance, important information, such as the cellular phone number, the carrier supplying service, subscription details, and security information. The SIM card 1049 serves primarily to identify the mobile terminal 1001 on a radio network. The card 1049 also contains a memory for storing a personal telephone number registry, text messages, and user specific mobile terminal settings.

Further, one or more camera sensors 1063 may be incorporated onto the mobile station 1001 wherein the one or more camera sensors may be placed at one or more locations on the mobile station. Generally, the camera sensors may be utilized to capture, record, and cause to store one or more still and/or moving images (e.g., videos, movies, etc.) which also may comprise audio recordings.

While the invention has been described in connection with a number of embodiments and implementations, the invention is not so limited but covers various obvious modifications and equivalent arrangements, which fall within the purview of the appended claims. Although features of the invention are expressed in certain combinations among the claims, it is contemplated that these features can be arranged in any combination and order. 

We claim:
 1. An apparatus comprising at least one processor and at least one non-transitory memory including computer program code instructions, the computer program code instructions configured to, when executed, cause the apparatus to: receive historical data of an event in which a first vehicle is impacted by a first tire pressure change at a first road segment, the historical data indicating point-of-interest (POI) information associated with the first road segment; and train a prediction model using the historical data, wherein the prediction model is configured to determine a likelihood in which a second road segment induces a second tire pressure change for a second vehicle based on POI information associated with the second road segment.
 2. The apparatus of claim 1, the historical data further comprising one or more other events in which one or more other vehicles is impacted by the one or more other pressure changes at the first road segment, one or more other road segments, or a combination thereof.
 3. The apparatus of claim 1, wherein the POI information associated with the first road segment indicates that the first road segment is: (i) proximate to a type of POI; (ii) directly connected to a private road segment of the POI; or (iii) a combination thereof.
 4. The apparatus of claim 1, wherein a first tire pressure change is defined by a predetermined rate of tire pressure drop.
 5. The apparatus of claim 1, wherein the historical data further indicate: (i) one or more map attributes associated with the first road segment; (ii) temporal information associated with the event; (iii) weather condition associated with the event; (iv) sensor data associated with the first vehicle during the event; (v) vehicle attribute data associated with the first vehicle; (vi) one or more road events within the first road segment during the event; or (vii) a combination thereof.
 6. The apparatus of claim 5, wherein the one or more map attributes indicate: (i) a functional class of the first road segment; (ii) whether the first road segment includes one or more pot holes; (iii) whether the first road segment includes one or more speed bumps; (iv) whether the first road segment includes a rail crossing; (v) whether a surface of the first road segment is paved or unpaved; (vi) a composition of the surface of the road segment; or (vii) a combination thereof.
 7. The apparatus of claim 5, wherein the temporal information indicates: (i) a duration of the event within a day; (ii) a date of the event; (iii) a season in which the event occurs; or (iv) a combination thereof.
 8. The apparatus of claim 5, wherein the sensor data indicate: (i) one or more tire pressure levels of the first vehicle; (ii) a speed of the first vehicle; (iii) an age of a tire of the first vehicle; (iv) a total distance traveled by the tire; or (v) a combination thereof.
 9. The apparatus of claim 5, wherein the vehicle attribute data indicate: (i) a size; (ii) a make; (iii) a model; (iv) a weight; (v) a classification; (vi) a type of tire equipped by the first vehicle; or (vii) a combination thereof.
 10. The apparatus of claim 5, wherein the one or more road events is: (i) one or more road works; (ii) one or more accident sites; or (iii) a combination thereof.
 11. The apparatus of claim 5, wherein the prediction model is configured to determine the likelihood based further on: (i) one or more map attributes associated with the second road segment; (ii) temporal information associated with a predicted event in which the second vehicle is predicted to traverse the second road segment; (iii) weather forecast data associated with the predicted event; (iv) vehicle attribute data associated with the second vehicle; (v) one or more road events within the second road segment; or (vi) a combination thereof.
 12. The apparatus of claim 1, wherein the first tire pressure change is defined by a plurality of tire pressure levels that is normalized based on: (i) a plurality of ambient temperature levels of the first vehicle during the event; (ii) a plurality of temperature levels of an environment in which the first vehicle is located during the event; (iii) a plurality of atmospheric pressure levels of the environment during the event; or (iv) a combination thereof.
 13. A non-transitory computer-readable storage medium having computer program code instructions stored therein, the computer program code instructions, when executed by at least one processor, cause the at least one processor to: cause a prediction model to determine a likelihood in which a first road segment induces a first tire pressure change for a first vehicle based on point-of-interest (POI) information associated with the first road segment, wherein the prediction model is trained using historical data, the historical data indicating an event in which a second vehicle is impacted by a second tire pressure change at a second road segment and POI information associated with the second road segment; and cause a notification indicating the likelihood on a user equipment associated with the first vehicle.
 14. The non-transitory computer-readable storage medium of claim 13, wherein the computer program code instructions, when executed by at least one processor, further cause the at least one processor to determine the likelihood based further on: (i) one or more map attributes associated with the first road segment; (ii) temporal information associated with a predicted event in which the first vehicle is predicted to traverse the first road segment; (iii) weather forecast data associated with the predicted event; (iv) vehicle attribute data associated with the first vehicle; (v) one or more road events within the first road segment; or (vi) a combination thereof.
 15. The non-transitory computer-readable storage medium of claim 14, wherein the historical data further indicate: (i) one or more map attributes associated with the second road segment; (ii) temporal information associated with the event; (iii) weather condition associated with the event; (iv) sensor data associated with the second vehicle during the event; (v) vehicle attribute data associated with the second vehicle; (vi) one or more road events within the second road segment during the event; or (vii) a combination thereof.
 16. The non-transitory computer-readable storage medium of claim 13, wherein the computer program code instructions, when executed by at least one processor, further cause the at least one processor to, responsive to the first vehicle traversing the first road segment: acquire sensor data associated the first vehicle; and train the prediction model based on the sensor data.
 17. The non-transitory computer-readable storage medium of claim 13, wherein the first road segment is a road segment of which the first vehicle has yet to traverse.
 18. A method of generating a route based on a road location predicted to induce a tire pressure change, the method comprising: receiving a destination for a vehicle as input; selecting a subset from a plurality of road segments as the route to the destination, wherein the subset is selected based on a likelihood in which one or more of the plurality of road segments induces a tire pressure change for the vehicle, wherein the likelihood is determined by a prediction model as a function of POI information associated with the one or more of the plurality of road segments; and outputting the route or a portion thereof.
 19. The method of claim 18, wherein the prediction model is trained using historical data, the historical data indicating an event in which one or more other vehicles is impacted by one or more other tire pressure changes at one or more other road segments from the plurality of road segments and POI information associated with the one or more other road segments.
 20. The method of claim 18, wherein the subset excludes the one or more of the plurality of road segments. 